import pymysql.cursors
import numpy as py
import pymysql
import pandas as pd
from sklearn.preprocessing import MinMaxScaler
from joblib import dump

class MysqlUtls(object):
    
    def __init__(self):
        self.conn=pymysql.connect(
            host='127.0.0.1',
            user='root',
            passwd='root',
            database='scenic',
            port=3306,
            charset='utf8'
        )
        
    def is_holiday(self,date):
        """是否节假日判断
        """
        if date in ['2024-09-03','2024-10-01','2024-10-02','2024-10-03','2024-10-04','2024-10-05','2024-10-06','2024-10-07','2025-01-01','2025-01-02','2025-01-03']:
            return 1
        return 0
        
    def get_scenic_data(self):
        cursor = self.conn.cursor(cursor=pymysql.cursors.DictCursor)
        sql = """
        SELECT DATE(g.create_time) as date,HOUR(g.create_time) as hour, count(*) as count 
        FROM order_user_gate_rel g WHERE HOUR(g.create_time) BETWEEN 6 and 26 GROUP BY date,hour
        """
            
        cursor.execute(sql)
        ret = cursor.fetchall()
        df = pd.DataFrame(ret)
        # print(df)
        #格式转换
        date_range = pd.date_range(start='2024-07-01',end='2025-03-01',freq='D')
        hours = range(6,24)
        full_index = pd.MultiIndex.from_product([date_range,hours], names=['date','hour'])
        df_full = df.set_index(['date','hour']).reindex(full_index,fill_value=0).reset_index()
        #按天组织数据，每行包含18个小时段的检票次数
        df_pivot = df_full.pivot(index='date',columns='hour',values='count')
        df_pivot['dow'] = df_pivot.index.dayofweek # 星期几（0-6）
        df_pivot['month'] = df_pivot.index.month #月份
        df_pivot['is_holiday'] = df_pivot.index.map(self.is_holiday)
        #print(df_pivot.head)
        #对星期几和月份进行独热编码
        df_pivot = pd.get_dummies(df_pivot,columns=['dow','month'], dtype=int)
            
        print(df_pivot)
            #归一化小时检票列
        hours_conlums = list(range(6,24))
        df_hours = df_pivot[hours_conlums].copy()
            
        feature_conlums = [col for col in df_pivot.columns if col not in hours_conlums]
        df_feature = df_pivot[feature_conlums].copy()
            
        scaler = MinMaxScaler()
        scaler_hours = scaler.fit_transform(df_hours)
        dump(scaler,'NN/scaler.joblib')
            
            #将归一化后的数据转换为DateFrame
        df_hours_scalerd = pd.DataFrame(scaler_hours,columns=hours_conlums,index=df_hours.index)
            
            #合并
        df_pivot_clean = pd.concat([df_hours_scalerd,df_feature],axis=1)
            #print(df_pivot_clean)
        df_pivot_clean.to_csv('NN/scenic_data.csv',index=False)
            
if __name__ == '__main__':
    mu = MysqlUtls()
    mu.get_scenic_data()